Protocol number 93-M-0170, NCT00001360 The Section on Functional Imaging Methods (SFIM) has advanced functional MRI (fMRI) methodology through development of processing and acquisition methods as well as research on the underlying mechanisms behind the fMRI signal. Our work over the past year has focused on methods to extract more information related to tasks, mental state, and individual traits from the fMRI time series. At high resolution we have more accurately mapped cortical layer connectivity and activity. Due to limited space, we decided to highlight here a few projects that are representative of our ongoing work. Multi-Echo Applications Our group has been pioneering the development and use of Multi-echo fMRI (ME fMRI). Because blood oxygen level dependent (BOLD) and non-BOLD signal fluctuations behave differently as a function of echo time, the unique ME fMRI acquisition of at least one echo allows for efficient and robust detection and removal of non-BOLD fluctuations from the time series. Our work developing and utilizing ME fMRI continues. In collaboration with Dr. Caballero-Gaudes, we are working on the detection of individual BOLD events with no information about the task timing or location. Preliminary tests show that the algorithm can reliably detect BOLD events associated with each individual trial in a manner similar to traditional GLM methods, yet without any information on paradigm timing. This new method has important applications including: uncovering hemodynamic events driving dynamic resting functional connectivity, and detecting inter-ictal events in epilepsy patients without the need for concurrent EEG recordings. Dynamic Functional Connectivity Dynamic functional connectivity (FC), understood as patterns of functional brain connectivity that change at the scale of seconds to minutes, has gained great attention in recent years. FC configurations can differ significantly from those obtained from entire time series, and recent studies suggest there is a link between these dynamics and individual behavior and behavioral traits. We previously demonstrated how whole-brain short-term FC patterns could be used to identify cognitive tasks. We are currently investigating how these patterns may provide useful behaviorally-relevant information about individual subjects to be used as a biomarker for specific traits. We are also investigating elements that drive these dynamics and differences in dynamics ranging from changes in arousal and engagement to changes in attention. Lastly, we are exploring the degree to which dynamics of specific networks can predict behavior. Towards the goal of deriving specific individual traits from FC network strengths, we had subjects perform a 2-back task, look for a visual target in a movie, and solve math problems. Performance was recorded on each task. Surprisingly, the sustained attention network (previously found) provided better prediction of performance than the 2-back-specific network. fMRI Connectivity vs. fMRI Magnitude Changes Our work on task-based connectivity change assessment has opened up an entirely new vista in fMRI processing. We have revealed that, for cognitive tasks, task based connectivity changes are more spatially extensive than magnitude changes and more specific or sensitive for a given task. This observation potentially opens a new subfield of brain mapping where connectivity changes are mapped rather than magnitude changes. We are actively exploring the robustness, repeatability, and task specificity of these observations. Naturalistic Tasks Traditional task-based fMRI experiments use tightly controlled paradigms that often lack ecological validity. Resting-state scans, on the other hand, are unconstrained, making it difficult to separate signal from noise. Naturalistic tasks, in which subjects view a movie or listen to a story in the scanner, may provide a happy medium for studying both group-level functional brain organization and individual differences. By imposing a standardized, time-stamped, and engaging stimulus on all subjects, naturalistic tasks evoke rich patterns of brain activity. These patterns lend themselves to flexible, data-driven analyses such as inter-subject correlation (ISC) and inter-subject functional connectivity (ISFC), which are model-free ways to isolate stimulus-dependent brain activity from spontaneous activity and noise. These techniques have several advantages over traditional approaches: 1) they do not requirea priorimodeling of specific task events and/or assumptions about the functional specificity of individual brain regions; 2) there is no need to assume a fixed hemodynamic response function; and 3) they allow for the characterization of the full spatiotemporal richness of both evoked and intrinsic brain activity. In our work, we hypothesize that individual differences in psychological traits - namely suspicion and paranoia - are able to be differentiated by observation of brain activity elicited by a naturalistic task involving a narrative describing a complex social scenario that was deliberately ambiguous regarding characters trustworthiness and intentions. The intent was that some subjects would interpret the story as more suspicious, and others as less so. The narrative did evoke a range of interpretations. We collected data from 23 healthy subjects as they listened to the pre-recorded narrative during fMRI scanning, and characterized their feelings and beliefs about the narrative. We found that pairs of subjects with more similar activity fluctuations in primary visual and visual association cortices--suggesting that they were engaging in more similar mental imagery during listening--were more likely to speak about the story in similar ways afterward. One ultimate goal of this line of work is to develop a neuroimaging-based stress test for use in both clinical and subclinical populations. Such a test would draw out individual differences beyond what can be observed at rest or using more traditional tasks, potentially leading to earlier identification of subjects at risk for mental illness or helping to guide interventions. Continuation of mapping layer-specific brain activity at 7T In the last year, we continued to develop acquisition fMRI methods that can measure brain activity with ultra-high resolution, aiming to distinguish activity across cortical layers. The novel parameter space explored since October 2016 includes: a. The necessity of physiological noise correction with sub-millimeter voxels where thermal noise dominates. b. The capabilities of layer-fMRI at ultra-high field strengths of 9.4T compared to 7T. c. The layer-dependent signal features across different cortical columns in M1 in a participant-specific surface-based signal evaluation scheme. d. The capabilities to image whole slices of the brain beyond the sensory motor system e. The capabilities of alternative contrast mechanisms, including spin-echo BOLD compared to blood-volume sensitive contrast and gradient-echo BOLD fMRI. f. Resting state connectivity from layers to the rest of the brain, in an attempt to arrive at directional input and causality across cortical networks.